mlsanityData to 14 Oct 2026

Towards Personalized Quantum Federated Learning for Anomaly Detection

arXiv 2511.07471 ↗First posted 8 Nov 2025cs.LG cs.CV quant-phcited by 4

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Abstract (arXiv)

Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum federated learning (QFL) overcomes these concerns by distributing model training among several quantum clients, consequently eliminating the requirement for centralized quantum storage and processing. However, in real-life quantum networks, clients frequently differ in terms of hardware capabilities, circuit designs, noise levels, and how classical data is encoded or preprocessed into quantum states. These differences create inherent heterogeneity across clients - not just in their data distributions, but also in their quantum processing behaviors. As a result, training a single global model becomes ineffective, especially when clients handle imbalanced or non-identically distributed (non-IID) data. To address this, we propose a new framework called personalized quantum federated learning (PQFL) for anomaly detection. PQFL enhances local model training at quantum clients using parameterized quantum circuits and classical optimizers, while introducing a quantum-centric personalization strategy that adapts each client's model to its own hardware characteristics and data representation. Extensive experiments show that PQFL significantly improves anomaly detection accuracy under diverse and realistic conditions. Compared to state-of-the-art methods, PQFL reduces false errors by up to 23%, and achieves gains of 24.2% in AUROC and 20.5% in AUPR, highlighting its effectiveness and scalability in practical quantum federated settings.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 38 of its 83 references that are in the map. First posted on arXiv 8 Nov 2025.

Builds on

38 of 83 references.

References · 38

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26 more
  1. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  2. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 127
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  13. 2021Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfercited by 23
  14. 2022Graph Anomaly Detection with Graph Neural Networks: Current Status and Challengescited by 23
  15. 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
  16. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  17. 2024Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approachcited by 11
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  19. 2022Quantum neural network autoencoder and classifier applied to an industrial case studycited by 8
  20. 2023Quantum Federated Learning With Quantum Networkscited by 8
  21. 2025Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approachcited by 8
  22. 2025Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noisecited by 8
  23. 2025Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learningcited by 7
  24. 2024False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learningcited by 5
  25. 2025SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualizationcited by 2
  26. 2024Reconstruction-Based Anomaly Localization via Knowledge-Informed Self-Trainingcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Escaping Barren Plateaus in Variational Quantum Algorithms Using Negative Learning Rate in Quantum Internet of Thingscited by 1
  2. 2025Towards Heterogeneous Quantum Federated Learning: Challenges and Solutionscited by 0
  3. 2026Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approachcited by 0
  4. 2026PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneitycited by 0